pip install https://download.pytorch.org/whl/cu90/torch-0.4.1-cp36-cp36m-linux_x86_64.whl
pip install torchvision
pip install tqdm
pip install tensorflow
pip install tensorboardX
pip install easydict
pip install scikit-tensor

# for pretraining CIFAR10/CIFAR100
$ python main_pretrain.py --learning_rate 0.1 --weight_decay 0.0002 --dataset cifar10 --epoch 200

# for pretraining Tiny-ImageNet
$ python main_pretrain.py --learning_rate 0.1 --weight_decay 0.0002 --dataset tiny_imagenet --epoch 300

# for pruning with EigenDamage, CIFAR10, VGG19 (one pass)
$ python main_prune.py --config ./configs/exp_for_cifar/cifar10/vgg19/one_pass/base/kfacf_eigen_base.json

# for pruning with EigenDamage, CIFAR100, VGG19
$ python main_prune.py --config ./configs/exp_for_cifar/cifar100/vgg19/one_pass/base/kfacf_eigen_base.json

# for pruning with EigenDamage, TinyImageNet, VGG19
$ python main_prune.py --config ./configs/exp_for_tiny_imagenet/tiny_imagenet/vgg19/one_pass/base/kfacf_eigen_base.json

# for pruning with EigenDamage + Depthwise separable, CIFAR100, VGG19
$ python main_prune_separable.py --config ./configs/exp_for_svd/cifar100/vgg19/one_pass/base/svd_eigendamage.json

@InProceedings{wang2019eigen,
  title = 	 {{E}igen{D}amage: Structured Pruning in the {K}ronecker-Factored Eigenbasis},
  author = 	 {Wang, Chaoqi and Grosse, Roger and Fidler, Sanja and Zhang, Guodong},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {6566--6575},
  year = 	 {2019},
  volume = 	 {97},
  publisher = {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/wang19g/wang19g.pdf},
  url = 	 {http://proceedings.mlr.press/v97/wang19g.html},
}